If you have the technical abilities required for the roles you’re seeking and have done a decent job compiling your application materials, you’ll eventually hear from employers that want to interview you.
You’ll want to be prepared for interviews once you start getting them. Your application materials have given you a chance, but the interview phase is where you close the deal and show potential employers that you are the best candidate for the Data Science job.
What Hiring Managers Are Looking For
During the interview process, hiring managers and recruiters are usually aiming to understand three basic facts about you:
How enthusiastic are you about the company and the position?
They want to see that you’re engaged in the company’s work and that you’ve already started thinking about how you could contribute value to this position.
How closely does your skill set correspond to the job’s requirements?
They want to see that you are technically qualified for the position. Because just because your résumé claims you know Python doesn’t guarantee you’re any good at it, almost every interview process will involve some technical skill testing. Employers are also looking for evidence of crucial soft skills such as communication.
Would you be a good ‘cultural fit’ for the company?
They want to know that your personality fits well with their corporate culture and that you can perform successfully and efficiently inside their teams and procedures. They also want to know if your career ambitions are compatible with the company and the role.
If you can satisfy the hiring manager and other team members on all three of these points at the end of your interviews, you’ll have a decent chance of earning a job offer.
How to Prepare for a Job Interview in Data Science
We’ll discuss specific example questions to study later in this article, but first, let’s talk about general Data Science Interview Questions and Answers preparation.
Consider the interview process to be akin to a big test at school: if you show up there without studying, you’re likely to fail.
You should review the following points before each interview:
This company has received your resume
Be prepared to answer questions regarding your background, work experience, and talents – anything on your CV could come up in an interview, and it won’t look good if your answer contradicts what you said on your resume.
Your Project Portfolio
You should know your projects inside and out, especially for entry-level positions. Prepare to answer questions about what you did, how you did it, and why you did it that way, as well as more general questions about the programming and statistics concepts you used in your projects (interviewers want to see if you just copied-pasted some cool code from StackOverflow or if you understand what’s going on).
Questions about the job description’s technical aspects
There’s no way to know ahead of time what technical questions or problems you’ll be asked to solve in an interview, but if the job description mentions any specific languages, methodologies, or talents, you can almost surely expect questions along those lines. Examine your knowledge to ensure that you understand not only how to do something, but when and why you might want to do it.
Personal inquiries about the position, your background, and your employment search.
Don’t just think you’ll be able to answer these questions off the top of your head! Even if some responses seem self-evident, it’s worth practicing answers to popular job interview questions and questions you expect based on your work experience before each interview.
The interview questions you intend to ask
If you are given the opportunity, you should prepare at least 3-5 questions to ask the interviewer. We’ll go over this in further depth later in this post, but preparing appropriate questions to ask requires some research and serious consideration of your function at this organization.
You’re not alone if you’re unsure where to begin your road to becoming a data scientist. Don’t be disheartened if it takes a long time or if your response rate is low. When applying for entry-level positions, this is extremely frequent. There is fierce rivalry for these posts, and the recruiting process might be arbitrary. Don’t give up!